Open Access   Article Go Back

Survey of Smart Data Acquisition System in Wind Turbine Machines Using Labview and IoT

D.Visali 1 , K. Muthulakshmi2

Section:Survey Paper, Product Type: Journal Paper
Volume-7 , Issue-10 , Page no. 137-143, Oct-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i10.137143

Online published on Oct 31, 2019

Copyright © D.Visali, K. Muthulakshmi . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: D.Visali, K. Muthulakshmi, “Survey of Smart Data Acquisition System in Wind Turbine Machines Using Labview and IoT,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.137-143, 2019.

MLA Style Citation: D.Visali, K. Muthulakshmi "Survey of Smart Data Acquisition System in Wind Turbine Machines Using Labview and IoT." International Journal of Computer Sciences and Engineering 7.10 (2019): 137-143.

APA Style Citation: D.Visali, K. Muthulakshmi, (2019). Survey of Smart Data Acquisition System in Wind Turbine Machines Using Labview and IoT. International Journal of Computer Sciences and Engineering, 7(10), 137-143.

BibTex Style Citation:
@article{Muthulakshmi_2019,
author = {D.Visali, K. Muthulakshmi},
title = {Survey of Smart Data Acquisition System in Wind Turbine Machines Using Labview and IoT},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {10 2019},
volume = {7},
Issue = {10},
month = {10},
year = {2019},
issn = {2347-2693},
pages = {137-143},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4909},
doi = {https://doi.org/10.26438/ijcse/v7i10.137143}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i10.137143}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4909
TI - Survey of Smart Data Acquisition System in Wind Turbine Machines Using Labview and IoT
T2 - International Journal of Computer Sciences and Engineering
AU - D.Visali, K. Muthulakshmi
PY - 2019
DA - 2019/10/31
PB - IJCSE, Indore, INDIA
SP - 137-143
IS - 10
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
322 232 downloads 160 downloads
  
  
           

Abstract

The embedded cum LabVIEW technology is now its prime and wealth of knowledge. Embedded technology plays a important role in integrating the various functions associated with it. This needs to tie up the various sources of the department in a closed loop system. This proposed system reduces man power, it also save time and operates efficiently without human interference. This project gives forth to first step for achieving the desired target. I have implemented Report generation & IoT unit for the wind turbine based industry for continuously acquiring the data and implementing the cryptography algorithm in it for security reasons.

Key-Words / Index Term

IoT, Lab VIEW, Report Generation

References

[1] Adel Nazemi Babadi , Mohammad Niyazi , Ronald A. Coutu, (2018) “Serviceability Optimization of the Next Generation Wind Turbines Using Internet of Things Platform” IEEE 2018 Smart Grid Conference (SGC), doi.org/10.1109/SGC.2018.8777861.
[2] Andrew Kusiak and Anoop Verma, (2012) “A Data-Mining Approach to Monitoring Wind Turbines” IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, Vol. 3, No. 1, pp.150 – 157.
[3] Chandra Bajracharya , Robin Grodi , Danda B. Rawat, (2015) “Performance Analysis of Wireless Sensor Networks for Wind Turbine Monitoring Systems” IEEE SoutheastCon 2015, doi.org/10.1109/SECON.2015.7133053.
[4] D.Kalyanraj, S.Lenin prakash, and S.Sabareswar , (2016) “Wind Turbine Monitoring and Control Systems Using Internet of Things” IEEE 2016 21ST century energy needs- materials, systems and applications (ICTFCEN). doi.org/10.1109/ICTFCEN.2016.8052714.
[5] Evangelos Papatheou, Nikolaos Dervilis, Andrew Eoghan Maguire, Ifigeneia Antoniadou, and Keith Worden, (2015) “A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm” IEEE transactions on industrial electronics, Vol.62, No.10, pp. 6636 – 6644.
[6] Grzegorz Swiszcz, Andrew Cruden, Campbell Booth and William Leithead, (2008) “A Data Acquisition Platform for the Development of a Wind Turbine Condition Monitoring System” IEEE 2008 International Conference on Condition Monitoring and Diagnosis, doi.org/10.1109/CMD.2008.4580521.
[7] Guoqian Jiang, Haibo He, Jun Yan, and Ping Xie, (2018) “Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox” IEEE transactions on industrial electronics, Vol.66, No.4, pp. 3196 – 3207 .
[8] Hae-Jin Sung, Byeong-Soo Go, and Minwon Park, (2019) “A Performance Evaluation System of an HTS Pole for Large-Scale HTS Wind Power Generators” IEEE transactions on applied superconductivity, Vol.29, No.5, doi.org/10.1109/TASC.2019.2908601.
[9] Haolin Yin, Rong Jia, Fuqi Ma, Dameng Wang, (2018) “Wind Turbine Condition Monitoring based on SCADA Data Analysis” IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC 2018). doi.org/10.1109/IAEAC.2018.8577523.
[10] Huan Long, Long Wang, Zijun Zhang, Zhe Song, and Jia Xu, (2015) “ Data-Driven Wind Turbine Power Generation Performance Monitoring” IEEE Transactions on Industrial Electronics, Vol.62, N0.10, pp. 6627 – 6635.
[11] Keun-Young Kang, Mohamed A. Ahmed and Young-Chon Kim, (2014)“Implementation of Condition Monitoring and Control System for Small-scale Wind Turbines” IECON 2014-40th annual conference of the IEEE industrial electronics society, doi.org/10.1109/IECON.2014.7048795.
[12] Long Wang, Zijun Zhang, Huan Long, Jia Xu, and Ruihua Liu, (2016) “Wind Turbine Gearbox Failure Identification with Deep Neural Networks” IEEE Transactions on Industrial Informatics, Vol.13, No.3, pp. 1360 – 1368.
[13] Peng Guo, (2012) “Wind Turbine Generator Bearing Condition Monitoring with NEST Method” IEEE 2012 24th Chinese control and decision conference (CCDC) doi.org/10.1109/CCDC.2012.6244033.
[14] Peng Sun, Jian Li, Yonglong Yan, Xiao Lei, Xiaomeng Zhang,(2014) “Wind Turbine Anomaly Detection Using Normal Behavior Models based on SCADA Data” IEEE 2014 ICHVE International Conference on high voltage engineering and application, doi.org/10.1109/ICHVE.2014.7035504.
[15] R. Morello, C. De Capua, G. Fulco , S.C. Mukhopadhyay, (2017)” A Smart Power Meter to Monitor Energy Flow in Smart Grids: The Role of Advanced Sensing and IoT in the Electric Grid of the Future” IEEE Sensors Journal, Vol. 17, No. 23, pp. 7828 - 7837.
[16] Ravi Kumar Pandit, David Infield, (2019) “SCADA based nonparametric models for condition monitoring of a wind turbine” The Journal of Engineering, Vol.2019, No.18, pp. 4723 – 4727.
[17] S. S. Tian, Z. Qian, L. X. Cao, (2016) “Wind Turbine Power Generation Performance Evaluation under Faults Condition” IEEE 2016 International Conference on Condition Monitoring and Diagnosis (CMD), doi.org/10.1109/CMD.2016.7757880
[18] Wilmar Hernandez, and Jorge L. Maldonado-Correa, (2017) “Power Performance Verification of a Wind Turbine by using the Wilcoxon Signed-Rank Test” IEEE transactions on energy conversion, Vol.32, No.1 , pp. 394 – 396.
[19] WU Xin, SU Liancheng , (2017) “Wind Turbine Modeling Research Based on the Combination of SCADA and Vibration Signals” IEEE 2017 4th International Conference on Information Science and Control Engineering(ICISCE), doi.org/10.1109/ICISCE.2017.277.
[20] Yan Pei, Zheng Qian, Siyu Tao, Hao Yu, (2018) “Wind Turbine Condition Monitoring Using SCADA Data and Data Mining Method” IEEE 2018 international conference on power system technology (POWERCON), doi.org/10.1109/POWERCON.2018.8601803.
[21] Zhixin Fu, Yang Luo, Chenghong Gu, Furong Li, Yue Yuan, (2018) “Reliability Analysis of Condition Monitoring Network of Wind Turbine Blade Based on Wireless Sensor Networks” IEEE Transactions on Sustainable Energy, Vol. 10 No. 2 ,pp. 549 – 557.